Journal of Grid Computing最新文献

In the ever-evolving landscape of smart city transportation, effective traffic management remains a critical challenge. To address this, we propose a novel Smart Traffic Management System (STMS) Architecture algorithm that combines cutting-edge technologies, including Blockchain, IoT, edge computing, and reinforcement learning. STMS aims to optimize traffic flow, minimize congestion, and enhance transportation efficiency while ensuring data integrity, security, and decentralized decision-making. STMS integrates the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm with Blockchain technology to enable secure and transparent data sharing among traffic-related entities. Smart contracts are deployed on the Blockchain to automate the execution of predefined traffic rules, ensuring compliance and accountability. Integrating IoT sensors on vehicles, roadways, and traffic signals provides real-time traffic data, while edge nodes perform local traffic analysis and contribute to optimization. The algorithm’s decentralized decision-making empowers edge devices, traffic signals, and vehicles to interact autonomously, making informed decisions based on local data and predefined rules stored on the Blockchain. TD3 optimizes traffic signal timings, route suggestions, and traffic flow control, ensuring smooth transportation operations. STMSs holistic approach addresses traffic management challenges in smart cities by combining advanced technologies. By leveraging Blockchain’s immutability, IoT’s real-time insights, edge computing’s local intelligence, and TD3’s reinforcement learning capabilities, STMS presents a robust solution for achieving efficient and secure transportation systems. This research underscores the potential for innovative algorithms to revolutionize urban mobility, ushering in a new era of smart and sustainable transportation networks.

Microservices has become a buzzword in industry as many large IT giants such as Amazon, Twitter, Uber, etc have started migrating their existing applications to this new style and few of them have started building their new applications with this style. Due to increasing user requirements and the need to add more business functionalities to the existing applications, the web applications designed using the microservices style also face a few performance challenges. Though this style has been successfully adopted in the design of large enterprise applications, still the applications face performance related issues. It is clear from the literature that most of the articles focus only on the backend microservices. To the best of our knowledge, there has been no solution proposed considering micro frontends along with the backend microservices. To improve the performance of the microservices based web applications, in this paper, a new framework for the design of web applications with micro frontends for frontend and microservices in the backend of the application is presented. To assess the proposed framework, an empirical investigation is performed to analyze the performance and it is found that the applications designed with micro frontends with microservices have performed better than the applications with monolithic frontends. Additionally, to predict the performance of microservices based applications, a machine learning model is proposed as machine learning has wide applications in software engineering related activities. The accuracy of the proposed model using different metrics is also presented.

The lack of data security and the hazardous nature of the Internet of Vehicles (IoV), in the absence of networking settings, have prevented the openness and self-organization of the vehicle networks of IoV cars. The lapses originating in the areas of Confidentiality, Integrity, and Authenticity (CIA) have also increased the possibility of malicious attacks. To overcome these challenges, this paper proposes an updated Games-based CIA security mechanism to secure IoVs using Blockchain and Artificial Intelligence (AI) technology. The proposed framework consists of a trustworthy authorization solution three layers, including the authentication of vehicles using Physical Unclonable Functions (PUFs), a flexible Proof-of-Work (dPOW) consensus framework, and AI-enhanced duel gaming. The credibility of the framework is validated by different security analyses, showcasing its superiority over existing systems in terms of security, functionality, computation, and transaction overhead. Additionally, the proposed solution effectively handles challenges like side channel and physical cloning attacks, which many existing frameworks fail to address. The implementation of this mechanism involves the use of a reduced encumbered blockchain, coupled with AI-based authentication through duel gaming, showcasing its efficiency and physical-level support, a feature not present in most existing blockchain-based IoV verification frameworks.

Abstract

In recent years, internet enterprises have transitioned from traditional monolithic service to microservice architecture to better meet evolving business requirements. However, it also brings great challenges to the resource management of service providers. Existing research has not fully considered the request characteristics of internet application scenarios. Some studies apply traditional task scheduling models and strategies to microservice scheduling scenarios, while others optimize microservice deployment and request routing separately. In this paper, we propose a microservice instance deployment algorithm based on genetic and local search, and a request routing algorithm based on probabilistic forwarding. The service graph with complex dependencies is decomposed into multiple service chains, and the open Jackson queuing network is applied to analyze the performance of the microservice system. Data evaluation results demonstrate that our scheme significantly outperforms the benchmark strategy. Our algorithm has reduced the average response latency by 37%-67% and enhanced request success rate by 8%-115% compared to other baseline algorithms.

Educational institutions today are embracing technology to enhance education quality through intelligent systems. This study introduces an innovative strategy to boost the performance of such procedures by seamlessly integrating machine learning on edge devices and cloud infrastructure. The proposed framework harnesses the capabilities of a Hybrid 1D Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) architecture, offering profound insights into intelligent education. Operating at the crossroads of localised and centralised analyses, the Hybrid 1D CNN-LSTM architecture signifies a significant advancement. It directly engages edge devices used by students and educators, laying the groundwork for personalised learning experiences. This architecture adeptly captures the intricacies of various modalities, including text, images, and videos, by harmonising 1D CNN layers and LSTM modules. This approach facilitates the extraction of tailored features from each modality and the exploration of temporal intricacies. Consequently, the architecture provides a holistic comprehension of student engagement and comprehension dynamics, unveiling individual learning preferences. Moreover, the framework seamlessly integrates data from edge devices into the cloud infrastructure, allowing insights from both domains to merge. Educators benefit from attention-enhanced feature maps that encapsulate personalised insights, empowering them to customise content and strategies according to student learning preferences. The approach bridges real-time, localised analysis with comprehensive cloud-mediated insights, paving the path for transformative educational experiences. Empirical validation reinforces the effectiveness of the Hybrid 1D CNN-LSTM architecture, cementing its potential to revolutionise intelligent education within academic institutions. This fusion of machine learning across edge devices and cloud architecture can reshape the educational landscape, ushering in a more innovative and more responsive learning environment that caters to the diverse needs of students and educators alike.

Workflows are special applications used to solve complex scientific problems. The emerging Workflow as a Service (WaaS) model provides scientists with an effective way of deploying their workflow applications in Cloud environments. The WaaS model can execute multiple workflows in a multi-tenant Cloud environment. Scheduling the tasks of the workflows in the WaaS model has several challenges. The scheduling approach must properly utilize the underlying Cloud resources and satisfy the users’ Quality of Service (QoS) requirements for all the workflows. In this work, we have proposed a heurisine-sensitive workflows in a containerized Cloud environment for the WaaS model. We formulated the problem of minimizing the MIPS (million instructions per second) requirement of tasks while satisfying the deadline of the workflows as a non-linear optimization problem and applied the Lagranges multiplier method to solve it. It allows us to configure/scale the containers’ resources and reduce costs. We also ensure maximum utilization of VM’s resources while allocating containers to VMs. Furthermore, we have proposed an approach to effectively scale containers and VMs to improve the schedulability of the workflows at runtime to deal with the dynamic arrival of the workflows. Extensive experiments and comparisons with other state-of-the-art works show that the proposed approach can significantly improve resource utilization, prevent deadline violation, and reduce the cost of renting Cloud resources for the WaaS model.

This study tackles the problem of increasing efficiency and scalability in deep neural network (DNN) systems by employing collaborative inference, an approach that is gaining popularity because to its ability to maximize computational resources. It involves splitting a pre-trained DNN model into two parts and running them separately on user equipment (UE) and edge servers. This approach is advantageous because it results in faster and more energy-efficient inference, as computation can be offloaded to edge servers rather than relying solely on UEs. However, a significant challenge of collaborative belief is the dynamic coupling of DNN layers, which makes it difficult to separate and run the layers independently. To address this challenge, we proposed a novel approach to optimize collaborative inference in a multi-agent scenario where a single-edge server coordinates the assumption of multiple UEs. Our proposed method suggests using an autoencoder-based technique to reduce the size of intermediary features and constructing tasks using the deep policy inference Q-inference network’s overhead (DPIQN). To optimize the collaborative inference, employ the Deep Recurrent Policy Inference Q-Network (DRPIQN) technique, which allows for a hybrid action space. The results of the tests demonstrate that this approach can significantly reduce inference latency by up to 56% and energy usage by up to 72% on various networks. Overall, this proposed approach provides an efficient and effective method for implementing collaborative inference in multi-agent scenarios, which could have significant implications for developing DNN systems.

Advancing in communication systems requires nearby devices to act as networks when devices are not in use. Such technology is mobile edge computing, which provides enormous communication services in the network. In this research, we explore a multiuser smart Internet of Vehicles (IoV) network with mobile edge computing (MEC) assistance, where the first edge server can assist in completing the intense computing jobs from the vehicular users. Many currently available works for MEC networks primarily concentrate on minimising system latency to ensure the quality of service (QoS) for users by designing some offloading strategies. Still, they need to account for the retail prices from the server and, as a result, the budgetary constraints of the users. To solve this problem, we present a Dueling Double Deep Q Network (D3QN) with an Optimal Stopping Theory (OST) strategy that helps to solve the multi-task joint edge problems and minimises the offloading problems in MEC-based IoV networks. The multi-task-offloading model aims to increase the likelihood of offloading to the ideal servers by utilising the OST characteristics. Lastly, simulators show how the proposed methods perform better than the traditional ones. The findings demonstrate that the suggested offloading techniques may be successfully applied in mobile nodes and significantly cut the anticipated time required to process the workloads.

Edge computing has emerged as an innovative paradigm, bringing cloud service resources closer to mobile consumers at the network's edge. This proximity enables efficient processing of computationally demanding and time-sensitive tasks. However, the dynamic nature of the edge network, characterized by a high density of devices, diverse mobile usage patterns, a wide range of applications, and sporadic traffic, often leads to uneven resource distribution. This imbalance hampers system efficiency and contributes to task failures. To overcome these challenges, we propose a novel approach known as the DRL-LSTM approach, which combines Deep Reinforcement Learning (DRL) with Long Short-Term Memory (LSTM) architecture. The primary objective of the DRL-LSTM approach is to optimize workload planning in edge computing environments. Leveraging the capabilities of DRL, this approach effectively handles complex and multidimensional workload planning problems. By incorporating LSTM as a recurrent neural network, it captures and models temporal dependencies in sequential data, enabling efficient workload management, reduced service time, and enhanced task completion rates. Additionally, the DRL-LSTM approach integrates Deep-Q-Network (DQN) algorithms to address the complexity and high dimensionality of workload scheduling problems. Through simulations, we demonstrate that the DRL-LSTM approach outperforms alternative approaches regarding service time, virtual machine (VM) utilization, and the rate of failed tasks. The integration of DRL and LSTM enables the process to effectively tackle the challenges associated with workload planning in edge computing, leading to improved system performance. The proposed DRL-LSTM approach offers a promising solution for optimizing workload planning in edge computing environments. Combining the power of Deep Reinforcement Learning, Long Short-Term Memory architecture, and Deep-Q-Network algorithms facilitates efficient resource allocation, reduces service time, and increases task completion rates. It holds significant potential for enhancing the overall performance and effectiveness of edge computing systems.